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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Machine Learning Toolkit for Extreme Scale

Support Vector Machines (SVM) is a popular machine learning technique, which has been applied to a wide range of domains such as science, finance, and social networks for supervised learning. MaTEx undertakes the challenge of designing a scalable parallel SVM training algorithm for large scale systems, which includes commodity multi-core machines, tightly connected supercomputers and cloud computing systems. Several techniques are proposed for improved speed and memory space usage including adaptive and aggressive elimination of samples for faster convergence , and sparse format representation of data samples. Several heuristics for earliest possible to lazy elimination of non-contributing samples are considered in MaTEx. In many cases, where an early sample elimination might result in a false positive, low overhead mechanisms for reconstruction of key data structures are proposed. The proposed algorithm and heuristics are implemented and evaluated on various publicly available datasets

Source record↗
DOE OSTI2015

Model Analysis ToolKit

MATK provides basic functionality to facilitate model analysis within the Python computational environment. Model analysis setup within MATK includes: - define parameters - define observations - define model (python function) - define samplesets (sets of parameter combinations) Currently supported functionality includes: - forward model runs - Latin-Hypercube sampling of parameters - multi-dimensional parameter studies - parallel execution of parameter samples - model calibration using internal Levenberg-Marquardt algorithm - model calibration using lmfit package - model calibration using levmar package - Markov Chain Monte Carlo using pymc package MATK facilitates model analysis using: - scipy - calibration (scipy.optimize) - rpy2 - Python interface to R

Harp, Dylan R.↗
DOE OSTI2016

X-Ray Toolkit (XTK) Touch App v. 1.0.x.x

Windows 8 App for use in viewing, enhancing, and manipulating x-ray images. Intended for use by state/local government, federal agencies, and military in explosive ordinance disposal (EOD) applications.

Garretson, Justin↗
DOE OSTI2013

Simulation Toolkit for Renewable Energy Advanced Materials Modeling

STREAMM is a collection of python classes and scripts that enables and eases the setup of input files and configuration files for simulations of advanced energy materials. The core STREAMM python classes provide a general framework for storing, manipulating and analyzing atomic/molecular coordinates to be used in quantum chemistry and classical molecular dynamics simulations of soft materials systems. The design focuses on enabling the interoperability of materials simulation codes such as GROMACS, LAMMPS and Gaussian.

Sides, Scott↗
DOE OSTI2016

Mobile X-Ray Toolkit v. 1.0.x

XTK Mobile is a software application for acquiring and processing digital images. XTK Mobile runs on mobile phones and tablets running iOS and Android operating systems.

Gilbert, Christine↗